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With the establishment and rapid development of the China (Shanghai) Pilot Free Trade Zone (FTZ), the scale of enterprises in the zone has grown rapidly. This paper takes the actual needs of the Shanghai FTZ as the background, extracts key information such as entities from the Big Data related to Internet enterprises, further constructs the enterprise knowledge graph, and applies it to the supervision and service of the FTZ. The enterprise knowledge graph is constructed using the Neo4j graph database. To verify the effectiveness of the named entity identification and relationship extraction methods proposed in this paper, experiments were conducted to validate them, and both achieved good results. [ABSTRACT FROM AUTHOR]

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signal variations within epoch. To address these issues, a novel deep learning architecture named segmented attention network (SAN) is proposed in this paper. The architecture can be divided into feature extraction (FE) and time sequence encoder (TSE). The FE module consists of multiple multiscale CNN (MMCNN) and residual squeeze and excitation block (SE block). The former extracts features from multiple equal-length EEG segments and the latter reinforced the features. The TSE module based on a multi-head attention mechanism could capture the temporal information in the features extracted by FE module. Noteworthy, in SAN, we replaced the RNN module with a TSE module for temporal learning and made the network faster. The evaluation of the model was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDFX, and one clinical dataset from Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed model achieved the accuracy of 85.5%, 86.4%, 82.5% on Sleep-EDFX, MASS and HSFU, respectively. The experimental results exhibited favorable performance and consistent improvements of SAN on different datasets in comparison with the state-of-the-art studies. It also proved the necessity of sleep staging by integrating the local characteristics within epochs and adjacent informative features among epochs. [ABSTRACT FROM AUTHOR]

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram is able to discover fine-grained semantic patterns effectively and accurately. [ABSTRACT FROM AUTHOR]

Online comments have become an important tool for disabled guests because of lower physical movement requirements. In order to illustrate and evaluate disabled guests' decision-making characteristics, this paper has used two steps for studying: (1) Data mining technology to collect e-comments from C-trip (www.ctrip.com) of 97 hotels in the cities of Beijing, Shanghai, Hangzhou and Guangzhou. Over 260,000 words were collected and analyzed by using ROSTcm software for this research. (2) Examined the relationship between the credibility and the perceived risk of disabled guests for their behavioral intentions. The result of this study has showed that: (1) Disabled guests pay more attention to the hotel barrier-free facilities, hotel barrier-free facilities, hotel personalized service, location accessibility, and the attitude and atmosphere of the hotel. (2) Disabled guests show positive attitude towards the hotel, and a neutral attitude accounting for 25.86% which indicates that the hotel industry still has much improving room especially with regards to disabled accessible guestrooms and accessibility. (3) Electronic Word-of-Mouth (eWOM) source credibility has a great influence on the overall perceived risk of the review of disabled guests because they are more likely for seeking internet comments for their lodging decision-making than other common guests. eWOM rational management can reduce the overall potential risk for disabled guests. (4) Hotels can enhance disabled guests' decision-making by advanced eWOM management. The study illustrated that disabled guests who used eWOM could be better managed and reduce potential risks when making decisions. As a result, this study also found that with better management of eWOM, it could help to meet the potential market for the disabled guests and at the meanwhile attract more customers because of higher social reputation. [ABSTRACT FROM AUTHOR]

After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed. [ABSTRACT FROM AUTHOR]

This study seeks to decipher the modus operandi of Shanghai's image portrayal to the audience through social media platforms, particularly Twitter. Leveraging the potency of big data analysis, the study scrutinizes the applicability of the two-step flow communication model in the communication of Shanghai's image. The findings highlight the cardinal role of opinion leaders in the image communication process, overshadowing the impact of mass media. The age-old phenomenon of two-step flow, wherein information trickles down from mass media to opinion leaders, appears to be fading away. Although mass media's tweets can potentially reach a broad audience, they do not necessarily captivate the attention of opinion leaders. Thus, the study underscores the importance of both mass media and opinion leaders in shaping Shanghai's image. [ABSTRACT FROM AUTHOR]

Patent classification has long been regarded as a crucial task in patent information management and patent knowledge mining. In recent years, studies combining deep learning automatic patent classification methods with deep neural networks have significantly increased. Although great efforts have been made in the patent deep classification task, they mainly focus on information extraction from a single view (e.g., title or abstract view), but few studies concern multi-view deep patent classification, which aims to improve patent classification performance by integrating information from different views. To that end, we propose a reliable multi-view deep patent classification method. Within this method, we fuse multi-view patent information at the evidence level from the perspective of evidence theory, which not only effectively improves classification performance but also provides a reliable uncertainty estimation to solve the unreliability of classification results caused by property differences and inconsistencies in the different patent information sources. In addition, we theoretically prove that our approach can reduce the uncertainty of classification results through the fusion of multiple patent views, thus facilitating the performance and reliability of the classification results. The experimental results on 759,809 real-world multi-view patent data in Shanghai, China, demonstrate the effectiveness, reliability, and robustness of our approach. [ABSTRACT FROM AUTHOR]

Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle's travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms. [ABSTRACT FROM AUTHOR]

Since the society enters the information age, science and technology have changed with each passing day. Electronic information has deeply affected social development and people's life. Based on the statistical correlation algorithm, this study analyzes the establishment model of statistical correlation algorithm. Then, the feature extraction technology of electronic information is obtained according to the statistical correlation algorithm, which promotes the development of electronic information., Afterwards, the feature extraction methods of electronic information among four cities of Beijing, Shanghai, Chongqing and Tianjin are compared and analyzed in detail. The experimental results show that the electronic information feature extraction method based on statistical correlation can be well applied in four municipalities directly under the central government, which provides some theoretical and experimental support for the electronic information feature extraction method in our country. [ABSTRACT FROM AUTHOR]

From the perspective of military intelligence work, the rise and widespread use of the network has opened up new horizons for intelligence acquisition, but the continuous increase in the amount of data in the network has made traditional intelligence analysis stretched. Therefore, enriching and developing military intelligence analysis methods have certain practical significance to make up for the shortcomings in current intelligence analysis. The traditional military intelligence analysis method cannot realize the in-depth mining and analysis of the network Shanghai information, obtain the deep intelligence knowledge required by the military, introduce the data mining technology into the military intelligence analysis and construct the network military intelligence analysis based on data mining model. The semantic analysis-based intelligence analysis algorithm in this model has certain superiority compared with the traditional association analysis, which can effectively improve the analysis efficiency and accuracy of military intelligence. [ABSTRACT FROM AUTHOR]

The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users' check-in behavior and the city's characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users' check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated. [ABSTRACT FROM AUTHOR]